Healthcare fraud is an organized crime which involves peers of providers, physicians, beneficiaries acting together to make fraud claims. Provider fraud is one of the biggest problems facing Medicare. This model predicts the potentially fraudulent providers based on the claims filed by them and also tries to discover what are the most important variables helpful in detecting the behaviour of potentially fraud providers so that we may flag such claims and perform in depth investigation on them. Healthcare fraud is not only adversely affects the insurance provider companies in terms of money but also the genuine claim settlement cases. Also the major challenge is the cost of determining or predicting potential fraud in any claim submitted is very high because if it is done incorrectly it may irritate legitimate customers and it may result in delayed claims adjudication.
Input variables : Claim ID, Beneficiary ID, Attending Physician, Operating Physician, Diagnosis Code, Procedure Code, Provider, Claim Amount, Claim Dates , Admission Dates , Date of birth, Gender, State, County, Premedical Conditions
Output Variables : Is Provider Potential fraud (Yes/No)
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Claims Processed | $ Saved | FWA Rate | FWA by CPT | FWA by Provider |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : rohansoni-jssaten2019.medium.com
| Model Category | : | Public |
| Date Published | : | October, 2020 |
| Healthcare Domain | : | Payer |
| Code | : | github.com |
| Claims Management |
| Fraud Waste and Abuse |